Google has remained a stable source of traffic to news publishers over the past year. Although many websites have seen their traffic significantly impacted by Google’s AI Overviews, Chartbeat data shows that for 565 U.S. and UK news publishers:
Search referrals made up 19% of traffic in July, little changed since early 2019.
Google dominates search traffic: 96% of publisher referrals.
Yes, but. “Search” here includes Google Discover, which is not traditional search. Discover is now the primary driver of Google referrals.
Why we care. Search traffic hasn’t collapsed. However, the stability is somewhat masked by a shift from traditional Google Search to Google Discover.
Direct traffic is shaky. Efforts to build a loyal, “type-in” audience have largely stalled, leaving publishers more dependent on Google and aggregators. Direct traffic to homepages and landing pages has fallen to 11.5% from a pandemic-era high of 16.3%.
Social keeps sinking. Social’s decline means fewer diversified referral sources:
Facebook referrals are down 50% since 2019, despite a recent bump.
X traffic is down 75% vs. 2019.
Only Reddit is surging – up 220% since 2019, boosted by Google visibility and an AI training deal (but it still sends less referrals than Facebook and X).
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/08/how-publisher-traffic-referral-types-are-stacking-up-T7pCfN.png?fit=1220%2C758&ssl=17581220http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-08-19 18:06:512025-08-19 18:06:51Google traffic to news publishers is steady, but it isn’t traditional Search
Generative AI is reshaping how people find information — but it hasn’t replaced search engines like Google. That’s according to a new Nielsen Norman Group study:
While users increasingly experiment with ChatGPT, Gemini and AI Overviews, most still default to old habits: starting with Google.
Why we care. Google is a habit – and habits are hard to break. That gives Google a built-in edge: even as AI eats into clicks, Google remains the default starting point for users. That means organic visibility still matters for brands and businesses. AI is reshaping the journey, but it won’t erase search overnight.
The big picture. According to the study:
AI overviews = fewer clicks. People notice and often rely on Google’s AI summaries, reducing the need to visit websites. Not new, and still bad news for publishers.
AI chat boosts efficiency. Once users tried Gemini or ChatGPT for complex tasks, they found them faster and more useful than traditional search.
Search isn’t gone. Even heavy AI users still cross-check with Google or visit content pages. No participant relied solely on AI for all information needs.
Familiarity wins. Just as “Google” became a verb, some users now casually call ChatGPT “Chat.” Brand familiarity may be the biggest advantage in AI search.
Bottom line. Generative AI is changing how people research – but it’s an evolution, not a revolution. The biggest barrier to AI adoption isn’t accuracy or UX, it’s human habit.
About the data. Nielsen Norman Group conducted remote usability testing with nine participants in North America and UK, representing diverse demographics and levels of AI experience. Sessions explored how users approached real research tasks with search engines and AI tools.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/08/generative-ai-search-google-5vSPei.webp?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-08-19 15:12:532025-08-19 15:12:53Generative AI is changing search, but Google is still where people start: Study
Is the number of clicks on your top-ranking content starting to slip? It’s time to find out where your traffic has gone and how to get it back.
AI Overviews are reshaping SERPs as we know them. Google now answers user queries directly in the SERP, and traditional blue links are getting pushed further down the page. You might still be ranking, but your visibility is shrinking.
This doesn’t mean SEO is dead. It just means the playbook has changed. To stay competitive, you need to understand how AI Overview optimization works and start building content designed to earn those coveted AI citations.
Key Takeaways
AI Overviews are rerouting traffic, not killing it. Your rankings may hold, but clicks drop because Google satisfies user intent directly in the SERP.
Answer-first content wins. Structuring pages with concise answers, logical headings, and clear formatting increases your chances of being cited in AI Overviews.
Authority signals matter more than backlinks. Brand mentions, topical trust, and consistent visibility across multiple platforms influence AI citations.
Owning your audience is your safety net. Diversifying channels and building first-party data ensures long-term visibility, even as search behavior evolves.
Why You’re Ranking But Still Losing Traffic
If your content still ranks in the top 10 SERP positions but traffic is slipping, there’s a good chance AI overviews are the culprit. Google’s AI-generated summaries dominate the top of the page, pushing organic listings below the fold. Users get the answer they want without ever clicking. In fact, almost 60% of Google searches end without users even making a single click.
You can see from this screenshot that when your search results load, there’s no organic results in site. In fact, in this instance, AI overviews even push sponsored results below the fold. This is the new reality of zero-click searches. Impressions might look steady, but clicks drop because users can satisfy their intent without leaving Google.
The solution is to stop thinking only in terms of traffic volume. Start focusing on visible influence: appearing in AI Overviews and being recognized as an authority, even when users don’t click.
AI Overviews And How They Are Turning The Funnel Upside-Down
Traditional search funnels start with discovery, move to consideration, and end in conversion. AI Overviews flip that script.
Users can start—and sometimes finish—their journey right on Google. With features like AI-generated summaries and featured snippets, the need to click through is lower than ever. Voice search and even short-form video integrations accelerate this shift, creating an environment where Google does the explaining for you.
For marketers, this means clicks are no longer the whole story. Your content has to deliver more than just clicks. It needs to capture attention inside the SERP and give users a reason to engage when they do click through. Strong on-page structure, engaging CTAs, and retention strategies like scroll-depth optimization now matter just as much as ranking. This is the essence of Search Everywhere optimization, which focuses on meeting users wherever they’re consuming content, not just on your site.
How To Optimize For AI Overviews
If you want your content featured in AI Overviews, you need to create pages that are easy for Google to summarize and trust. Here’s how to give Google what it wants:
Lead with an answer-first layout: Open your page with a concise, 2–3 sentence answer to the core query. This immediately gives AI a clear takeaway, increasing the odds of being cited in an overview. Expand into supporting details afterward with a logical flow.
Use structured formatting: Break your content into clean H2s and short paragraphs so Google can scan and interpret it quickly. Bulleted and numbered lists help AI extract step-by-step processes or summaries.
Add schema and FAQs: Implement FAQ and How-To schema to highlight your key answers for AI. Include a short FAQ section at the end of your article to increase your odds of citation for question-based queries.
Target long-tail, conversational keywords: AI Overviews thrive on natural, question-based searches. Integrate these phrases into headings and early sentences to align with how users talk to search engines and voice assistants.
Publish fresh, authoritative content: Share unique insights, proprietary data, or first-hand expertise to meet E-E-A-T signals—experience, expertise, authority, and trustworthiness. AI favors credible, original content over generic summaries.
Support with media: Embed YouTube videos, charts, or screenshots to improve engagement and reinforce authority. Use descriptive alt text so search engines can understand and reference your visuals.
Combining structure, authority, and clarity makes it easy for AI to pull your content and keep your brand visible in the new SERP landscape.
YouTube and Video: Your Shortcut to Visibility
Video content—especially on YouTube—is one of the fastest ways to gain visibility in AI Overviews. Google and Gemini favor YouTube because it’s part of their ecosystem, and AI models naturally pull from sources they already trust.
Short, keyword-focused videos can surface in AI-generated results even if your text content isn’t cited. A 60–90 second explainer video that directly answers the search query gives AI a clean snippet to work with while also boosting your chances of appearing in video carousels.
The charts below show just how effective video is. They show the categories of YouTube videos that have shown up in AI overviews and how fast the trend of videos showing up in AI overviews has grown over time.
Create concise, educational videos tied to core keywords.
Embed them on relevant blog posts or landing pages to reinforce topical authority.
Add captions or transcripts so AI models can understand and summarize your video content.
Video can reclaim lost search visibility while building multi-surface authority across AI-driven and traditional search.
Off-Page Signals Matter More Than Backlinks
In the age of AI Overviews, Google and AI models are looking beyond traditional backlinks. They increasingly value off-page signals like brand mentions and expert quotes in reputable sources.
AI models evaluate whether your brand is recognized and trusted across the web. A mention in an industry publication, a quote in a news article, or a stat cited in a whitepaper can be as impactful as a link for AI visibility.
To strengthen your off-page signals:
Pursue public relations (PR) opportunities in industry-relevant media and blogs.
Share original data or research that journalists and peers want to reference.
Encourage brand discussions on platforms like LinkedIn, Reddit, and Quora, which AI crawlers frequently mine. Internally, we’ve seen tremendous growth for our client, TurboTax, by helping them launch a branded Reddit campaign—including discussions and engagement.
The goal is to create a trustworthy footprint online. When AI sees your brand cited in multiple credible sources, you’re far more likely to be included in its summaries, even without a traditional backlink.
Build Topical Trust Across the Web
AI Overviews reward brands that show consistent authority on a topic, not just one-off content. Google and AI models look for a pattern: Are you producing relevant, high-quality content across multiple platforms that reinforces your expertise?
To build topical trust:
Publish blog posts, guides, and FAQs that cover your key themes in depth.
Share insights across social media and YouTube, giving AI more signals that your brand is active and authoritative.
Leverage user-generated content (UGC), like community discussions, testimonials, and real-world examples, to demonstrate authenticity.
Ensure your content aligns with E-E-A-T across every channel.
Maintaining a consistent and credible presence wherever your audience searches makes it easy for AI to recognize your brand as a reliable source. That recognition is what creates a trustworthy brand footprint that AI can work with.
You Need to Diversify Your Channels Now
Relying solely on Google for traffic is riskier than ever. The shrinking SERP visibility caused by AI overviews and zero-click searches means that even top-ranking content might not deliver the same ROI it once did.
To protect your brand, you need to diversify your traffic sources:
Combine SEO and paid search to maintain visibility and retarget your most valuable branded keywords.
Invest in social media, email, and YouTube to capture attention outside of Google.
Build a strategy that prioritizes owning your audience instead of depending on any single platform.
Diversifying channels doesn’t just protect your current visibility. It’s a great way to grow your online brand. A strong multi-channel approach captures leads you might otherwise miss, making you less vulnerable to Google’s constant evolution. Ultimately, the brands that thrive in the AI era are the ones that meet their audience everywhere, not just in search results.
When AI Overviews dominate search, the brands that win are the ones creating proprietary insights that can’t be found anywhere else. AI models favor content that provides original data because it signals authority and adds value beyond generic summaries. Internal research is your secret weapon.
Instead of relying solely on public stats, collect your own:
Run audience surveys to uncover trends or opinions in your niche.
Conduct polls or quizzes to generate quick, shareable insights that can be repurposed into blogs and social posts.
Analyze internal data like customer behavior, conversion trends, or product usage to produce unique reports.
Turn these findings into case studies and data-driven articles. Proprietary insights make your brand more likely to appear in AI Overviews and attract backlinks and press coverage, compounding your authority across the web.
FAQs
How do I optimize for AI Overviews?
Start with an answer-first structure: give a concise response in the first 2–3 sentences, then expand with supporting details. Use a clear structure with H2s and bulleted lists so Google can easily scan and summarize your content. Implement FAQ or how-to schema, and include a dedicated FAQ section to match AI’s preferred Q&A format. Fresh, authoritative content supported by brand mentions and backlinks will boost your chances of being cited.
How are AI Overviews changing the SERPs?
AI Overviews now dominate the top of Google results, pushing organic listings further down the page. This creates more zero-click searches, where users get answers without visiting your site. Even if your rankings haven’t changed, your visibility and clicks may decline. Making AI-friendly formatting and multi-channel strategies more important than ever.
Conclusion
There’s no need to panic. AI Overviews aren’t erasing traffic, they’re simply rerouting it. Your pages may still rank, but when Google’s summaries dominate the top of the SERP, visibility doesn’t always translate into clicks. The old playbook of relying on impressions and top rankings isn’t enough anymore.
To win in this era of search, your SEO strategy has to include AI overview optimization. Content needs to be structured for AI-first discovery, with clear answers and logical formatting that gains LLMs’ trust. Now, success is about building influence. When your brand appears in AI Overviews and consistently reinforces topical expertise, you maintain visibility even when users don’t land on your site.
The final step is ownership. Diversifying channels and leveraging Search Everywhere optimization gives your brand resilience, while first-party data ensures you can nurture and convert your audience on your own terms. If done right, AI can be your biggest opportunity, not just a threat.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-08-19 13:21:002025-08-19 13:21:00Where Did My Traffic Go? Winning In The Age of AI Overviews
AI search is evolving fast, but early patterns are emerging.
In our B2B client work, we’ve seen specific types of content consistently surface in LLM-driven results.
These formats – when structured the right way – tend to get picked up, cited, and amplified by models like ChatGPT and Gemini.
This article breaks down five content types gaining notable AI search visibility, what makes them effective, and how to optimize them for LLM discovery:
Comparison pages.
Integration docs/open APIs.
Use case hubs.
Thought leadership on external platforms.
Product docs with schema.
1. Comparison pages
Our analysis shows that Gemini frequently surfaces “X vs. Y” content in AI Overviews and AI Mode – even when the query doesn’t ask explicitly for the comparison.
What to include
Publish /vs/ pages with pros, cons, pricing, use case match, and schema.
Do this for any competitors that bring in a decent volume of comparison queries, along with any comparisons that are easily related to your product or service.
2. Integration docs/open APIs
Our analysis has provided numerous instances of GPTs and Copilot citing SaaS APIs and dev docs in answers.
Example
A ChatGPT prompt for “setting up span metrics for backend services” cited a docs page from performance monitoring company Sentry in a list of best practices.
What to include
Maintain clear documentation + changelogs with versioning and schema.
LLMs pick up posts from company experts, including founders, SMEs, and established thought leaders, on outlets like Medium and Dev.to for strategy-based questions.
Example
What to include
Syndicate posts from a company founder, SME, or brand ambassador with a unique POV, then include a canonical link back to the business website.
5. Product docs with schema
Gemini AI Mode lifts from product docs if they’re structured with FAQs, How-to sections, and/or breadcrumb structured data.
Example
What to include
Add FAQPage, HowTo, breadcrumb structured data, and SoftwareApplication schema types to product docs.
3 overarching recommendations
You should never veer from the E-E-A-T principles that have long underpinned traditional SEO. Those same tenets will serve you well for LLM discovery, too.
Beyond them, however, there are a few LLM-specific steps to consider if your goal is to increase AI search visibility.
I’ll break down three key recommendations.
Optimize for multi-modal support
AI search systems are increasingly retrieving and synthesizing multimodal content (think: images, charts, tables, videos) to better answer user queries.
Flex your content across multiple media types to provide more useful, scannable, and engaging answers for users.
Specific recommendations:
Ensure images and videos remain crawlable for search and AI bots.
Serve images via clean HTML and avoid lazy-loading with JavaScript-only rendering, since LLM-based scrapers may not render JavaScript-heavy elements.
Images should use descriptive alt text that includes topic context.
Add captions to images and videos with an explanation right below or beside the visual.
Use <figure>, <table>, etc., with contextually correct markup to help parse tables, figures, and lists.
Avoid images of tables. Use HTML tables instead for a machine-readable format supporting tokenization and summarization.
Optimize for chunk-level retrieval
AI search engines don’t index or retrieve whole pages.
They break content into passages or “chunks” and retrieve the most relevant segments for synthesis.
Optimize each section like a standalone snippet.
Specific recommendations:
Don’t rely on needing the whole page for context. Each chunk should be independently understandable.
Keep passages semantically tight and self-contained.
Focus on one idea per section: keep each passage tightly focused on a single concept.
Use structured, accessible, and well-formatted HTML with clear subheadings (H2/H3) for every subtopic.
AI search engines synthesize multiple chunks from different sources into a coherent response.
Aim to make your content easy to extract and logically structured to fit into a multi-source answer.
Specific recommendations:
Summarize complex ideas clearly, then expand (A clearly structured “Summary” or “Key takeaways”).
Start answers with a direct, concise sentence.
Favor a factual, non-promotional tone.
Use structured data to help AI models better classify and extract structured answers.
Use natural language Q&A format.
Create B2B content that wins in AI search
An added benefit of these five content types is that they span multiple intent stages – helping you attract prospects and guide them through the funnel.
Just as important: make sure your AI search measurement systems are in place (we use Profound, GA, and qualitative research) so you can track impact over time.
And stay tuned to reports and industry updates to keep pace with new developments.
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When it comes to AI-powered search, visibility isn’t just about ranking – it’s about being included in the answer itself.
That’s why generative engine optimization (GEO) matters. The same technical SEO practices that help search engines crawl, index, evaluate, and rank your content also improve your chances of being pulled into AI-generated responses.
The good news? If your technical SEO is already strong, you’re halfway there. The rest comes down to knowing which optimizations do double duty: improving your rankings while boosting your visibility in generative results.
This article breaks down four technical pillars with the biggest impact on GEO success:
Schema markup.
Site speed and performance.
Content structure.
Technical infrastructure.
1. Schema markup: Speaking AI’s language
Schema has long been essential for SEO because it removes ambiguity. Search engines use it to understand content type, identify entities, and trigger rich results.
For GEO, schema clarity is even more important. LLMs favor structured data because it reduces ambiguity and speeds extraction. If your content is marked up clearly, it’s more likely to be selected and cited.
Priority schema types for GEO
Focus on evergreen types that improve visibility:
FAQPage: Clearly labeled Q&A helps LLMs match user queries and surface your answers.
HowTo: Structured step-by-step processes are easy for AI to extract.
Product / Service: Defines pricing, availability, and specifications for accurate inclusion.
Article / NewsArticle with Author: Authorship adds a trust signal to your content.
Organization / LocalBusiness: Reinforces your identity, entity clarity, and local authority.
Review / AggregateRating: Provides social proof that AI engines use as quality signals.
VideoObject / ImageObject: Makes your multimedia easier for AI to find and feature.
BreadcrumbList: Improves context and page hierarchy mapping.
Implementation best practices
Use JSON-LD format (Google’s recommended approach).
Test rigorously with Google’s Rich Results Test and Schema Markup Validator.
Keep markup synced with your visible content – outdated schema erodes trust.
Don’t overdo it: mark up only what helps explain the content.
Bottom line: Schema improves your chances of being cited in AI answers, keeping competitors out of the box.
2. Site speed and performance: A (dis)qualifying factor
Generative engines pull from billions of pages. If yours is slow or unstable, they can skip it in favor of faster, more reliable sources.
Quick performance wins
Compress images; use WebP or AVIF; enable lazy loading.
Eliminate render-blocking CSS and JavaScript.
Target a server response time (TTFB) under 200ms.
Use a CDN to reduce latency.
Bottom line: Speed could be a tiebreaker between equally relevant sources. Faster pages have higher odds of inclusion in AI-generated answers – and they convert better once users click through.
3. Content structure: Making information machine-readable
LLMs rely on clarity. The easier it is for machines to parse and organize your content, the more likely it is to appear in AI-generated results.
JavaScript rendering: Don’t hide core content behind heavy client-side rendering. Use server-side rendering for anything essential.
Bottom line: If search or generative engines can’t crawl, verify freshness, or trust your site, your content won’t be considered – no matter how authoritative it is.
Building for search and AI success
The technical elements that drive GEO success aren’t new. They build on SEO fundamentals you already know:
Schema.
Performance.
Structure.
Infrastructure.
But in the AI era, these aren’t just best practices – they’re the deciding factors between being featured and being forgotten.
Getting this right will preserve your search visibility and put your content at the center of AI-driven answers.
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Google’s AI results are changing everything about how local businesses get discovered—and reviews are now at the center of it all. They shape visibility, build trust, and, when leveraged effectively, drive conversions.
In this live webinar, GatherUp VP of Marketing Mél Attia and renowned Local SEO expert Miriam Ellis will share never-before-seen research findings on how AI and consumer behavior are reshaping local SEO. You’ll discover:
How Google’s AI-powered results are prioritizing local businesses
What consumers really care about when evaluating businesses
Why reputation and reviews are the ranking lever most agencies underutilize
New consumer data, benchmarks, and tactical frameworks to boost your clients’ results
Whether you’re helping clients gain visibility, prove trustworthiness, or turn reviews into revenue, this session will equip your agency with actionable insights—and a narrative that makes review strategy impossible to ignore. You can save your seat here!
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Search is changing fast. This year, we’ve seen more instances of search engine results sharing space with AI-powered features that are changing how people find information.
Along with the changes to how search engines display information, we’re also seeing users explore new methods to search for information. Google AI Mode, Gemini, ChatGPT, Perplexity – there are many large language models (LLMs) capturing users’ attention, providing new ways for users online to discover and make decisions about your brand.
Customer sentiment, shown through reviews and ratings, is becoming a key part of both local and branded search.
For brands looking to stay ahead, focusing on sentiment, review ratings, and authority signals will be key. These are the items that not only affect rankings but also impact what shows up in search snippets and LLM responses.
LLMs like Google’s AI Mode are pulling together and highlighting customer sentiment within their responses when asked about specific brands or for geo-modified search queries, think “home repair near me”.
For businesses, paying attention to their review strategy and reputation will be key to standing apart in local results, overall organic visibility, and showing up favorably in AI responses. However, even with these changes, many of the tried-and-true best practices that have helped brands succeed in local search in the past still apply.
Searches with local intent: Google’s AI Mode
When it comes to local search, “near me” queries continue to be highly important. In traditional search, these typically trigger a Local Pack followed by organic blue links.
In Google’s AI Mode, the experience is similar. Users are shown a list of local businesses, often with short descriptions, star ratings, and review summaries.
The links cited are usually citation platforms like Yelp or TripAdvisor, business websites, or publications, and it’s common to find Google Business Profile place cards. Clicking these opens the familiar Google Business Profile interface, keeping users within the Google ecosystem.
What does this mean for businesses aiming to capture visibility in AI-driven local search results? Many of the foundations of local SEO still apply.
NAP consistency: Ensure your business name, address, and phone number (NAP) are accurate and consistent across all listings.
Citations: Maintain listings on trusted third-party sites like Yelp, TripAdvisor, and local directories to help reinforce credibility.
Google Business Profile optimization: Fully complete and regularly update your profile with accurate info, photos, business hours, and relevant categories.
Reviews: Generate and respond to reviews to build trust and signal relevance to both users and search engines.
Branded search results for local businesses
When searching for a local business using branded terms in AI Mode, it’s common to see many of the same elements and data sources as traditional search. These business overviews often include a description of the company, the products or services offered, and customer sentiment.
Often, the customer sentiment section summarizes review data pulled from multiple sources, such as TripAdvisor, Yelp, industry-specific sites such as Apartments.com, and Google Business Profile.
What’s unique about AI Mode is that it provides unbiased summaries of pros and cons about a business based directly on available customer reviews, which can come directly from Google Business Profile or be a mixed of review data from trusted online sources. These clear overviews include overall sentiment and often link to the business profiles.
AI Mode isn’t the first time Google has experimented with review summaries.
Some industries, like restaurants, already have “Review Summaries” in organic search results. These generative AI summaries highlight Google Business Profile review data, usually with a more positive tone, alongside the star rating and list of reviews.
The importance of reviews
Reviews shape how your brand appears online, whether they are displayed front and center on your Google Business Profile or surfaced as snippets in responses from LLMs. Google’s AI Mode, ChatGPT, and Perplexity all returned some information or mention of customer reviews when searching for local businesses, especially for branded queries.
These responses emphasize how both positive and negative offline experiences can influence what is said about your brand online and the importance of customer perception, especially when those experiences get highlighted for customers who may be discovering your brand for the first time.
Businesses need to pay attention to reviews, if not across all platforms, then at least on Google Business Profile. Review data is being pulled into AI-driven results and also plays a role in local search visibility.
“Prominence means how well-known a business is. Prominent places are more likely to show up in search results. This factor’s also based on info like how many websites link to your business and how many reviews you have. More reviews and positive ratings can help your business’s local ranking.”
How can businesses adapt?
By following the tactics local businesses should already be doing to succeed in local search:
Focus on generating new, recent reviews.
Respond to both positive and negative reviews.
Read reviews to understand the strengths and weaknesses of your business. Seeing a trend in negative reviews? That could indicate it’s time to make some changes and address those weaknesses.
Monitor brand mentions not just for backlinks but also to understand what people are saying about your business online, including community forums, social media platforms, and online publications.
In addition to traditional review sites, platforms like Reddit, TikTok, and Quora are showing up more frequently in branded and local search results. These conversations are also being picked up and summarized in tools like Perplexity and ChatGPT. That means the things people are saying about your business in comment threads or short-form videos can influence how your brand is being represented across both organic and AI-powered results.
What else can be done:
Look closely at how your business is perceived online and do the same for your competitors.
Compare your review count and average star rating to those of businesses showing up alongside you in the Local Pack. How does your business stack up?
Check how AI tools like LLMs or Google’s AI Mode describe your competitors during branded searches and identify where they source that information.
Try asking AI tools to compare your business and a competitor. The way these tools summarize differences can give insight into strengths, weaknesses, and areas where you may need to improve to stay competitive in the market.
LLM data sources
LLMs pull from a range of online sources to build summaries about businesses. For local and branded search queries, much of the information they use closely mirrors what shows up in traditional organic search results. This includes data from:
Google Business Profiles.
Third-party review sites.
Official business websites.
Wikipedia.
Online directories and aggregators.
News articles.
Public conversations on forums or social media.
LLMs don’t use the same ranking algorithm as Google Search, but they rely on much of the same publicly available information.
Why this matters:
The efforts businesses make to improve local SEO, such as maintaining accurate listings, collecting reviews, and building authority, also help shape how their brand is represented in AI-generated search results.
Reinforces the importance of managing your presence across multiple platforms and staying aware of where your brand is mentioned.
Highlights trusted third-party sites where your business may be listed but not actively managed. These listings still influence visibility and should not be overlooked.
Identifies which platforms are trusted within your specific industry, revealing opportunities to strengthen your presence on niche or vertical-specific sites.
Managing reputation at scale for multi-location businesses
For multi-location and microbrand businesses, managing sentiment at the local level adds another layer of complexity. It is not just about how the overall brand is perceived, but how each location appears in search results. This is especially important for industries like senior living, apartment communities, and healthcare, where customer experience and trust are crucial in decision-making.
A few negative reviews tied to a single location can shape perception across the board. That is why reputation strategies need to scale while still staying localized. Each location needs a clear plan to monitor feedback, respond to reviews, and build a strong presence in both traditional and AI-powered search results.
Core local SEO principles remain
Search is evolving fast, and we can expect more LLMs and AI-powered features to continue to shape how information is delivered to users.
Customer sentiment and brand perception are now more important in shaping how a business appears online, whether it’s in traditional organic search results or another platform.
Why?
Because perception matters, both online and in real life. Tools like Google’s AI Mode, Perplexity, Gemini, and ChatGPT are putting reviews, ratings, and sentiment summaries front and center, making customer feedback more visible than ever.
Now is the time for brands to take a close look at how they appear in LLMs, understand the feedback being surfaced, and identify areas to improve. Doing this not only helps with visibility in AI-driven search but also strengthens your local market presence.
As part of a broader brand reputation and visibility strategy, it’s essential to regularly monitor how your business is showing up in both traditional and AI-powered search results. That includes checking branded SERP features like AI Overviews, People Also Ask, video carousels, and social content pull-ins. These elements shift often, and staying aware of what’s being surfaced helps inform both SEO and reputation efforts.
You don’t need to reinvent the wheel. To keep up with the changing search landscape, you just need to focus your efforts in the right direction.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/08/running-store-near-me-google-ai-mode-FbvBhZ.png?fit=767%2C622&ssl=1622767http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-08-18 18:03:342025-08-18 18:03:34Want to win at local SEO? Focus on reviews and customer sentiment
More of your customers are using AI to research products before they buy. Are you prepared?
To put this into perspective:
Last year, you might’ve searched “best bed sheets” on Google and scrolled through a few links or a Shopping ad.
This year, you’re asking ChatGPT:
“I sleep hot and have sensitive skin. Can you recommend some breathable bed sheets that won’t irritate me?”
Totally different input. Totally different rules for showing up.
AI Search still cares about the fundamentals — content, crawlability, internal links, and high-quality backlinks. But now, your visibility is influenced by more than just your website.
AI models reflect the full picture:
What people say about your brand
Where you’re mentioned
How your product is reviewed
It’s not just keyword targeting — it’s relevance engineering.
Shoutout to Mike King @ iPullRank for coining this term.
That’s where AI Search Optimization comes in.
In this guide, you’ll learn how to:
Make your product pages visible and understandable to LLMs
Structure your data with schema and product feeds
Submit your catalog to AI search platforms
Shift from keyword targeting to prompts and personas
Build an AI-friendly brand presence across the web
Track your visibility in a probabilistic, answer-first world
The future of ecommerce search isn’t about rankings. It’s about being part of the answer. This guide will show you how.
Step 1: Make Your PDPs Crawlable and Renderable
Before you do anything, start here: can bots actually see your product content?
When people started taking AI tools and chatbots seriously in 2022/23, some site owners turned to blocking their crawlers from accessing their site.
But if you block the crawler, it won’t be able to serve your pages in its responses.
Don’t Block AI Crawlers in Your Robots.txt File
Unless you actively took the step to block them, you shouldn’t need to do anything here. But it’s still worth verifying there are no lines in your robots.txt file like:
User-agent: GPTBot
Disallow: /
Don’t Serve Important Content Using JavaScript
The other aspect of crawlability to consider is how you’re serving your content.
If it’s not in the raw HTML, LLMs like these can’t see it. And if they can’t see it, you won’t show up in AI-generated product recommendations.
To make sure you’re not causing crawling issues here, you first need to understand how your ecommerce platform handles JavaScript. Every platform is different:
Shopify: Generally fine, but watch out for third-party apps injecting schema or content via JS.
WooCommerce: Depends heavily on your theme. Many use plugins that load parts of the page with JS.
Custom stacks: If you’re using React, Vue, or similar frameworks, check whether product pages render server-side or after load.
Next, check your PDPs manually. You can do this by right-clicking and selecting “Inspect” in your browser.
Then press Command+Shift+P on Mac, or Control+Shift+P on Windows/Linux.
In the Command Menu, start typing “javascript” and then select “Disable JavaScript”:
Reload the page, and you’ll see how it looks without JavaScript enabled — in other words, how LLMs like ChatGPT see the page:
In the Nike example above, the LLM would still see key info like the product title, description, and price.
But in the example below…
…it would see nothing.
You can see on the right that there’s still page code loading. But nothing is actually displayed to the user with JavaScript disabled. Meaning AI tools wouldn’t be able to pull any info from this page.
If you are using apps or components that rely on JavaScript to display key content, talk to your dev team about server-side rendering (SSR) or prerendering. The goal is to ensure all critical product info is delivered in the first HTML response.
Once your product pages are crawlable, the next step is making them understandable.
Structured data — specifically Schema.org markup in JSON-LD format — helps systems like ChatGPT, Perplexity, and Google understand what your product is, how much it costs, whether it’s in stock, and more.
In the world of SEO, we’ve long used schema markup to improve how our pages appear in traditional search results.
Here’s an example of a traditional Google results enhanced with schema markup, appearing as a rich snippets:
But for LLM visibility, schema helps the AI tools understand key details about your products. Which makes it easier for them to pull in your products when they’re making recommendations for users.
How do we know this?
Because Microsoft has told us. The tech giant, a major investor in OpenAI (behind ChatGPT), said:
“[Structured data] makes it easier for search engines not only to index your content, but to surface it accurately and richly in search results, shopping experiences, and AI-driven assistants.”
(Interestingly, Microsoft/Bing recommends combining this with IndexNow — a service that automatically pings search engines when you update your content.)
Plus, using structured data just makes sense — it helps make it easier for complex machines to understand our content. Whether that’s a search engine or an LLM, providing more context is generally always going to be a good idea.
Here’s how to use structured data to improve your ecommerce store’s LLM visibility:
Focus on Product Pages First
While there’s value in marking up other templates (like category pages, blog posts, or FAQs), your product pages are where it counts most.
This is the data that LLMs and search engines will use to:
Associate your product with relevant categories and attributes
Match your offering to long-tail purchase prompts
Feed structured knowledge into their product and shopping systems
Here are the fields to include:
@type: Product
GTIN, SKU, MPN
Brand
Description
Offer block (price, currency, availability, URL)
Review/rating info if available
Use your schema to reflect reality, not just fill fields. But also add as much context as you can.
If your product is eco-friendly, US-made, sweatproof — encode it. The better your markup, the more context LLMs have to surface your product in nuanced prompts.
Make sure the schema is present in the raw HTML — not loaded with JavaScript.
Bonus: Extend to Reviews, FAQs, HowTo
Once your product markup is solid, consider adding:
Review and AggregateRating blocks
FAQPage markup for your PDPs or Help Center
HowTo schema for tutorial content or sharing post-purchase use cases
These all help build context around your product and can influence how LLMs present or recommend it.
Once you’ve marked up your product pages, the next step is scaling an effective structure across your entire catalog. That’s where a high-quality product feed comes in.
Step 3: Build a High-Quality Product Feed
Structured feeds have been essential for Google Shopping, Meta Advantage+, and TikTok Shop for a while.
And now, they’re becoming equally important for AI-powered discovery. Especially as platforms like Perplexity and OpenAI build out product recommendation systems.
Think of your feed as the dataset LLMs will eventually pull from when answering questions like this:
Perplexity has launched a Merchant Program accepting feed uploads, called the Perplexity Merchant Program. This lets ecommerce sellers have even more control over how their products can appear in AI responses.
Plus, OpenAI is quietly testing ways to let store owners upload feeds to improve their AI responses for product recommendations.
These feeds will likely drive future AI shopping experiences across chat, search, and even voice interfaces.
So how do you set your product feeds up in an LLM-friendly way?
What to Include
To optimize your product feeds for AI, start with the essentials:
Product title
Description
Price
Availability
Product URL
GTIN or MPN + Brand
Image URL
Note: Tools like ChatGPT may still generate their own versions of some of these (like titles). But it’ll still typically use information from places like your product feeds to inform its responses.
After you’ve added the basics, layer in high-value fields like:
Category or taxonomy
Color, material, and size variants
Shipping cost and speed
Review count and star rating
Custom labels for campaigns or segmentation
Use the same language your customers use.
This means writing product information the way your customers actually talk and search, not how your internal teams or suppliers describe things. For example:
Instead of:
“Athletic footwear with moisture-wicking synthetic upper”
Write:
“Running shoes that keep your feet dry”
How do you find out how they talk?
Look at your customer reviews, support tickets, and search queries that already drive traffic to your store.
For example, they might search for “cozy sweater” not “knitted pullover.” This can inform your title and description choices.
How to Submit Product Feeds to LLMs
Here’s how to submit your product feeds for three of the biggest AI interfaces.
Perplexity:
In 2024, Perplexity launched their Merchant Program. This fuels the platform’s shopping experience for Pro users. Your products may appear in carousel-style answers and shopping-focused prompts, and shoppers can buy without leaving Perplexity.
You can find out more about the program and sign up here.
OpenAI (ChatGPT):
OpenAI is piloting product discovery via ChatGPT’s “Search + Product Discovery” initiative. They’re exploring using uploaded feeds to power future buying experiences inside ChatGP.
Google’s Merchant Center feeds power Shopping Ads, organic Shopping listings, and likely influence how Google’s AI systems interpret and surface your products in AI Mode and AI Overviews.
Step 4: Monitor LLM Crawlers
Once you’ve put all the steps in place to make your ecommerce store crawlable by LLMs, the next step is to make sure they’re actually accessing your content and product pages.
Here’s how to do that:
Set Up Bot Monitoring
Use server logs or your CDN (like Cloudflare, Fastly, or Akamai) to track requests from:
GPTBot: This user agent is used by OpenAI to crawl web content that may be used in training their generative AI foundation models.
OAI-SearchBot: Used by OpenAI to link to and surface websites in search results in ChatGPT’s search features.
PerplexityBot: Identifies Perplexity’s AI search crawler when it accesses websites.
Google uses various Googlebot user agents to crawl the web, depending on the type of content being crawled (e.g., desktop, mobile, images). You can find a detailed list of common Googlebot user agent strings and their purposes in resources from Google for Developers.
For each of these bots, track:
Which pages they’re crawling (PDPs, collection pages, sitemap, feed)
How often they come back
How crawl patterns evolve over time
This helps confirm they’re discovering your content and gives you a baseline to measure progress.
Step 5: Shift from Keyword Lists to Prompts and Personas
Keyword research is still important. But you also need to think about how your customers are likely to prompt AI tools when looking for products like yours.
LLMs answer questions, interpret context, and make recommendations based on how people naturally speak.
That means you need to rethink how you optimize for product discovery. Not by keywords alone, but by personas, use cases, and prompt formats.
Start With What You Know
Your best-performing SEO and paid search keywords are still the foundation. They tell you:
Which products and categories convert
How people describe their intent in short-form searches
Use these to anchor your prompt strategy — but expand outward.
Think in Prompts, Not Just Queries
As people become more savvy with how AI tools work, more and more shoppers are going beyond just typing in “best bed sheets.” They’re asking:
Medium-length prompts:
“Best cooling sheets for hot sleepers”
“Softest bed sheets under $100”
“What kind of sheets stay on the bed all night?”
Longer, context-rich prompts:
“I’m a side sleeper who gets hot at night. What bed sheets will stay cool and not cling to my skin?”
“Looking for breathable, hypoallergenic sheets that work well in humid climates”
“I have sensitive skin and eczema. What’s a good sheet material that won’t irritate me?”
Your goal is to build context around your products that lines up with this kind of language and framing.
Note: You can’t predict exactly what your customers will ask, and there are infinite ways they can do it. But thinking about prompts — not just keywords — will put you in a good place to be able to optimize your ecommerce pages for LLMs.
Map Your Catalog to Prompt-Based Use Cases
Think in layers:
By need: cooling, breathable, wrinkle-resistant, organic
By persona: hot sleeper, allergy sufferer, luxury buyer, college student
By situation: new apartment, guest bedroom, summer refresh, wedding registry
By problem: sheets come loose, feel scratchy, trap heat, shrink in the wash
This is how you start to think of your items like answers and solutions, not just products.
Use These Prompts to Guide Content and Merchandising
Let this prompt structure inform your:
Product page copy and comparison points
Blog posts and videos
Social media posts
FAQs and Help Center content
Category names and filters
Product feed descriptions and attributes
LLMs can pull from all of it — so make sure you’re using the kind of language your real customers use everywhere.
Step 6: Seed Your Brand Across the Web
Even if your site is crawlable, your schema is perfect, and your feed is super optimized — LLMs still learn about your brand based on what people are saying about you elsewhere.
They’re trained on massive web-scale datasets, so third-party content — like reviews, Reddit mentions, YouTube transcripts, forums, blog posts — can carry as much (or more) weight than your owned channels.
If you want to show up in AI answers, your brand needs to already exist in the wider conversation.
Where You Want to Show Up
AI tools like ChatGPT, Perplexity, and Claude all lean on third-party review sites and forums in their answers to brand and product-related queries.
These are the places you’ll want to show up in order to be included in those answers:
Review sites: Trustpilot, Amazon, Google Reviews, BBB, niche review sites
Reddit, Quora, & niche forums: Participate in threads and subtly seed your product category (without being spammy)
YouTube: Appear in titles, transcripts, and product comparisons — even if you’re not the creator (consider partnering with creators to do this)
Affiliate content: Get included in roundups, listicles, and side-by-side comparisons
Showing up in these places is half the battle. The other component is how you show up.
Ideally, you’ll want to be mentioned alongside competitors (“like Brooklinen but…”). And in the right, relevant context (“these are some of the best cooling sheets for eczema”).
A lot of this is going to be completely out of your control (especially on platforms like Reddit). But good marketing practices can make it more likely that people will naturally talk about your brand in the way you want them to.
This Is Just Good Marketing
Gaining LLM visibility is a byproduct of an effective multichannel marketing strategy.
If you’re running a strong content program, building brand awareness, and actively participating in your category — you’re already seeding relevance.
What’s new is the urgency: LLMs are already using these signals to decide which brands deserve to be recommended.
Related: See our LLM Seeding Playbook for tactics, templates, and outreach strategies.
Step 7: Track Your AI Search Visibility
In traditional SEO, visibility was deterministic: rank #1 for a keyword, get X% of clicks.
That model is breaking.
AI-powered discovery works differently. Your brand might appear in one version of a response, but not the next.
Whether your ecommerce store is included depends on how the user phrases their prompt, how much brand recognition you have, and how often you’re referenced across the web.
So, your measurement strategy needs to adapt.
What to Track
Start by building a prompt library — real questions your customers might ask:
Organize prompts by topic (e.g., cooling sheets, organic materials, luxury bedding)
Group them by persona (e.g., hot sleepers, allergy sufferers, budget-conscious buyers)
Then choose a tool to test visibility: like Semrush AI SEO Toolkit, Peec.AI, or Profound
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-08-18 14:02:292025-08-18 14:02:29How to Optimize Your Ecommerce Store for AI Search (7 Steps)
A few months back, one of my clients pinged me on Slack and said:
“We keep hearing on sales calls that ChatGPT says we don’t offer a feature we’ve had for years! How can we fix this?”
Sure enough, when prompted, ChatGPT confidently responded, “No, the platform does not have that feature, but this other competitor does!”.
For obvious reasons, this was worrying for the client.
Not only was ChatGPT spreading misinformation about their product, it was actively pitching an alternative solution.
The source of the misinformation: A single old blog post that hadn’t been updated in two years.
How many potential buyers decided not to book a sales call because of this?
How many had discovered a new competitor instead?
This issue signals a large shift in how bottom-of-funnel product research is done.
Before: Your website was the source of truth.
It was your “always on” salesperson. You kept your homepage and product pages fresh, and that was where buyers did their digging.
Now: Large language models (LLMs) are a product research assistant. A new touchpoint at a critical stage in the buying journey.
They’re the modern day gatekeepers, acting as the layer between you and your target audience, communicating on your behalf.
And their source of info? It’s often sources you’d forgotten even existed.
As marketers, it falls to us to make sure LLMs are communicating the right things in the right way about our products and services.
In this article, I’ll show you the 7-step playbook my team is developing to tackle this challenge — what we’re calling Branded Generative Engine Optimization (GEO).
Free resource: For step 6, we’ve created a handy spreadsheet to help you ideate common questions. Download it here.
What is Branded GEO?
Branded GEO is the process of making sure conversational AIs and LLMs give accurate, helpful, and up-to-date answers about your brand. It focuses on branded prompts and queries.
This targets a highly valuable audience segment, including those who are:
In the market to buy a solution or service like yours
Already know you are a viable option and are exploring your offer
This segment is showing the highest intent — they’re asking questions about your product, and they’re using your brand name in their prompts.
Like branded SEO, branded GEO is easier to influence. It’s more actionable than trying to optimize for broad industry queries. For that reason, it’s a fantastic starting point if you want to explore GEO.
Note:Generative engine optimization is the broader practice of optimizing for AI-powered search systems like ChatGPT, Claude, and Google’s AI Overviews. Branded GEO is a specific subset focused on branded queries.
For the following exercise, I’ll use ChatGPT as the LLM and the B2B SaaS product, Airtable, as an example.
Airtable has recently undergone some serious positioning and product pivots, so it illustrates the new challenges of branded GEO.
Let’s start with a quick setup.
Step 1: Set Up Your LLM
Head to ChatGPT and turn on temporary mode. This avoids any personalization skewing your results.
Also turn on the “search” feature — this ensures ChatGPT is accessing information after June 2024 when it was last trained.
This is currently the data we can influence.
Step 2: Enter Your First Branded Prompt
Next, prompt ChatGPT with a simple question: “What is [your brand name]?”.
Here are the results for Airtable:
Step 3: Analyze the Response
Pay attention to how ChatGPT describes your product and company.
Is it accurate? Is it how you would describe your company?
Or do things need to change?
With Airtable, we see what must be a frustrating situation playing out.
Airtable pivoted in June 2025, shifting away from their “super powerful spreadsheet” positioning and relaunching as an:
“AI-native app platform, where the magic of vibe coding meets enterprise reliability and the scalability of AI agents”.
That’s quite the change. And ChatGPT hasn’t caught up yet.
Here’s how Airtable positions themselves versus how ChatGPT does:
How Airtable describes themselves
How ChatGPT describes Airtable
Website: “Next gen app building platform”
“cloud-based, no-code platform”
Website: “Deploy thousands of agents inside your apps”
“simplicity of a spreadsheet with the power of a relational database”
Homepage meta title: “AI App Building for Enterprise”
“hybrid spreadsheet‑database”
LinkedIn page: “AI-Native App Platform”
Common use cases: “Project management”
Luckily, most readers are unlikely to see such a drastic mismatch.
But at the current rate of technological innovation, almost all companies are undergoing continuous reinvention, and so you are likely to find outdated features and positioning.
Step 4: Find the Source of Misinformation
In this step, we start to tackle the misinformation by looking for its source.
We usually find that ChatGPT has sourced its information from:
An outdated article
A LinkedIn page that hasn’t been updated in three years
A landing page that reflects the “old you”
A hallucination due to completely missing information on that topic
As a quick example, I was recently living in Melbourne, and ChatGPT picked that up from a LinkedIn post and stated that my agency, Spicy Margarita, was founded in Melbourne. (We’re based in the UK).
Despite my travel plans, I wasn’t keen to be positioned as an Australian company, so I quickly removed that mention of Melbourne, and ChatGPT’s response adapted.
To address the misinformation you find, visit the sources used and look for a match between the language used by ChatGPT and the words on the page.
See that it says you cost $1,000? Find the source that says that and update it. Fixing the issue is often this simple (unless there is hallucination, which we address in the next step).
To operationalize this process, collate all the sources driving misinformation into a spreadsheet and note down:
Whether that source should be deleted or updated
Specific text that needs to be changed
Specific text that needs to be added — for example, if a feature is missing, you can spell it out in the sources
For our Airtable example, we can see that a highly trusted source (Wikipedia) is currently out of date.
If we worked for Airtable, we’d start with the Wikipedia article. They should note this down and edit this page with their new positioning as soon as possible.
As a major, trusted source of internet knowledge, updating Wikipedia is likely to help influence LLMs, but it may not fix the positioning issue in one fell swoop.
Step 5: Publish, Update, or Delete Sources
For smaller brands with a relatively small web footprint, we find this task is more straightforward.
Take your latest positioning, messaging, and features, and make sure they are represented in key sources LLMs are referencing. Ideally, refresh every source that mentions your brand — from social media accounts to on-site and off-site web pages.
Brands with a larger web presence will find this task more challenging.
If, like Airtable, you have outdated articles written about you across 100s of websites you don’t control, outreach may need to be operationalized to update or take down those sources. If you have no luck with that, we’d suggest running a new campaign that seeds LLMs with lots of new sources that contain your up-to-date information.
Given sources like Zapier and Airtable’s own starter guide (pictured below) still have their old positioning, there’s more work to do.
Here’s the branded GEO adjustment we would make for Wikipedia:
Airtable’s Wikipedia Before
Airtable’s Wikipedia After
“Airtable is a spreadsheet-database hybrid, with the features of a database but applied to a spreadsheet. The fields in an Airtable table are similar to cells in a spreadsheet, but have types such as ‘checkbox’, ‘phone number’, and ‘drop-down list’, and can reference file attachments like images.”
“As of June 2025, Airtable now operates as an AI-native app platform, enabling users to build, edit, and automate production-ready business apps through natural-language prompts via its AI assistant Omni and embedded Field Agents.”
You may also find that LLMs are hallucinating something entirely. This can’t be fixed by updating or removing a source. This often happens because they didn’t find an answer in any sources.
If LLMs are hallucinating an answer, you’ll want to try to influence the answer by creating a source that answers the question with the correct information.
Start building a content roadmap with new topics to cover, directly answering those key questions your target buyer has.
These can be hosted on your blog or help center, and serve dual purposes: for branded GEO and as helpful sales material.
Step 6: Expand Your Branded Question Prompts
So far, we’ve asked just one question about your brand.
But, prospective customers are likely asking many, many questions that you’ll want to monitor.
Unfortunately, exact data on those questions is still not available.
Prompts are unlike traditional keywords. They’re often longer and more personalized. However, that doesn’t mean we can’t optimize for the less long-tail prompts and hope that bleeds through.
We can make educated guesses at the topics LLM users are asking questions about using six methods:
1. Ask Your Inbound Leads
I ask every inbound lead who found me via ChatGPT what their prompts and journey were. One even pulled the conversation up and read the exact prompt back to me — it said “I want an SEO agency in the B2B space who is staying up-to-date with AI,” and our agency came up.
This kind of insight is gold dust.
It shows you how your audience prompts, what issues they face, and what content and GEO efforts of yours are already working.
A similar technique is to look in sales insights platforms like Gong for mentions of ChatGPT and to encourage your sales team to ask the question for you.
2. Start With Common Questions
Begin with general questions that people ask about brands. Then, tailor those questions to fit your specific situation.
We’ve made a spreadsheet template to help you find the questions people ask AI about your brand.
3. Use a Keyword Research Tool
Head to your keyword research tool of choice and enter your brand name.
In Semrush’s Keyword Magic Tool, you can filter on “Questions” to pull a full list of the questions people are asking about your brand.
Find questions that someone considering your product might ask.
For example, these are a few I’d select for the Airtable before their pivot. Each question factors into the purchase decision.
Questions
is airtable free
how much does airtable cost
how much does airtable enterprise cost
is airtable only for apple
is airtable a crm
does airtable have a desktop app
can airtable send emails
does airtable integrate with outlook
can airtable be integrated into wordpress
can airtable be integrated with shopify
does airtable have an api
4. Use Google Autocomplete
Another helpful tool for finding audience questions is Google Autocomplete.
You’ll find autocomplete is a part of normal Google Search. It anticipates and suggests search queries as you type, making predictions based on popular searches, your location, and your search history (so do this in incognito mode).
Enter these queries to see what people are asking:
Is [brand name]
How [brand name]
Does [brand name]
Where [brand name]
When [brand name]
What [brand name]
You can get more suggestions by adding each letter of the alphabet afterward, too. Like this:
To speed things up, I recommend taking screenshots of each autocomplete and uploading them all to ChatGPT for extraction and grouping.
5. Use ChatGPT Autocomplete
If you’re lucky enough to be represented in ChatGPT autocomplete already (at the time of writing, only very large brands are), this is also a place to dig into.
6. Talk to Your Sales and Support Teams
When we do this exercise with clients, we run a Q&A session with both the sales team and customer support teams.
This first-party insight is invaluable for predicting the questions your target audience has.
Here are six top questions from our client questionnaire:
What common questions about your product do you get from prospects on sales calls?
What do prospects misunderstand or get wrong before speaking to you?
What common objections about your brand do you get from prospects?
Do prospects ever mention ChatGPT and what they found there?
What questions do people typically ask in your website chat about [brand name]?
What usually triggers prospects to book a call or sign up for [brand name] now?
Step 7: Repeat
Now you’ve gathered your questions, it’s time to see how LLMs answer them and fix up the answers.
To do this, repeat steps 1-5.
Tracking the Impact of Branded GEO Work
The impact of branded GEO is twofold:
Relief: From knowing you’re being accurately represented by LLMs.
Additional Conversions: From removing inaccuracies and misinformation, adequately filling content gaps in your lower sales funnel, and better informing buyers before they join sales calls.
To track the impact of this exercise, we recommend:
Monitoring LLM output: Take your list of questions and compare the before and after. Monitor those regularly to confirm continued accuracy.
Track conversion metrics: Compare key conversion rates (sign-ups, demo requests, sales) before and after your LLM content improvements. I suggest you add a “Where did you hear about us?” to your sales booking forms to closely monitor leads that started in LLMs.
Sales team feedback: With the example in the introduction of this article, the sales team had been facing misinformation issues. If you’ve faced a similar issue, stay in close contact with them so get a pulse check on the impact.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-08-14 13:59:422025-08-14 13:59:42Branded GEO: How to Control What AI Says About Your Brand
The rules of app store optimization (ASO) are changing. What was once a tactical discipline focused on rankings and keywords is rapidly evolving into a strategic lever for user acquisition, brand visibility, and sustained app growth. With advancements in AI, shifts in search behavior, and the rise of hyper-personalized discovery, ASO is entering a new era, one that is contextual, intelligent, and continuously adaptive.
In this article, we explore the forces shaping the future of ASO, from AI-driven metadata and personalized search to voice discovery and predictive app visibility. If you’re looking to stay competitive in an increasingly saturated app landscape, this is what you need to know next.
Key Takeaways
ASO is shifting from tactical to strategic. It’s no longer just about keyword stuffing or climbing the rankings. The future is intelligent, personalized, and performance-driven.
AI is rewriting how metadata works. Expect real-time, AI-powered updates that align with shifting user behavior, not quarterly refreshes based on guesswork.
Search is getting personal. Two users can type the same keyword and see different results. Your listings need to adapt to individual intent, not the average user.
Customized Product Pages (CPPs) are just the beginning. Soon, app store experiences will be dynamic, predictive, and unique to each user journey.
Voice and ambient discovery are rising. People are finding apps through voice assistants and predictive surfaces, not just typed search queries.
App Intents will drive next-gen visibility. Apps need to signal what they do, for whom, and when—so platforms can surface them at just the right moment.
Success depends on adaptability. ASO teams that test fast, personalize creatively, and embrace AI will outperform those still chasing static rankings.
Where App Discovery is Heading Next
App Store Optimization is no longer just about rankings. As mobile ecosystems evolve and user expectations shift, the future of ASO will be defined by personalisation, predictive relevance, and deeper integration with emerging technologies.
We are entering an era where search is increasingly contextual, discovery is increasingly intelligent, and store listings behave more like adaptive marketing assets than static storefronts.
This section explores the trends shaping the future of ASO. From AI-powered metadata and personalized search to voice discovery and App Intents, we will unpack what marketers need to prepare for now, and where the next growth opportunities lie.
AI-Powered Metadata: From Static Copy to Intelligent, Performance-Driven Content
As AI becomes more embedded in the app ecosystem, metadata is evolving from something manually updated every quarter to a fluid, data-informed asset that adapts to audience trends, behaviour, and market shifts.
Instead of relying solely on guesswork and human intuition, AI is enabling metadata to be:
Continuously optimized based on live performance signals
Automatically localised for language, phrasing, and cultural nuance
Tailored dynamically for different cohorts and user segments
What This Means For Marketers
Metadata is no longer a static exercise in copywriting. AI allows marketers to test, learn, and iterate faster than ever before. With platforms like Apple and Google increasingly rewarding contextual relevance and behavioral alignment, brands will need to adopt:
AI-assisted keyword selection that reflects shifting user intent
Predictive copywriting that forecasts what combinations are likely to convert
Automated content scoring to prioritise which changes to make first
Strategic Implication
In the future of ASO, teams may move from monthly metadata refreshes to near-continuous optimization. Success will depend not just on creativity, but on how well marketers collaborate with AI tools to generate, score, and deploy high-performing content at scale.
AI will not replace ASO specialists, but it will raise the bar for relevance, speed, and strategic experimentation.
Personalized Search: The Shift From Relevance For All to Relevance For Me
In 2025, search is no longer a one-size-fits-all experience. Platforms are increasingly using on-device signals and behavioral patterns to tailor search results to individual users. This means that two users searching the same keyword may now see completely different apps.
This change brings enormous potential for marketers. With personalization comes the ability to surface your app in more targeted, contextually relevant ways – if your metadata, creatives, and reviews align with the user’s specific needs.
What’s Driving It:
The rise of personalized search is being fueled by increasingly sophisticated data inputs. App stores now consider user history, download behavior, device-level preferences, and even time-of-day patterns when determining what results to show. Rather than relying solely on keyword matching, search algorithms are layering in contextual data like app usage, cross-app engagement, and location signals to surface the most relevant content to each user.
What Approach Marketers Should Take:
Build out multiple value propositions and tailor your messaging for distinct segments
Focus on creative variety – consider how different screenshots or CTAs might resonate differently
Track shifts in keyword performance that may signal emerging personalized search patterns
Localize not just for language, but for lifestyle and behavior trends in key markets
Strategic Insight
In a world of personalized search, brands that maintain a single, static value proposition will lose ground. The winners will be those who treat the store listing like a modular experience, ready to adapt to any user, any context, at any time.
The Future of Smarter Acquisition
As acquisition costs rise and attention spans shrink, smarter acquisition has become a brand imperative. What CPP(customized product pages) represent today, a personalized, intent-driven storefront, may soon evolve into real-time, AI-curated experiences that respond dynamically to user segments, behavioral signals, and even market trends.
App stores ranking pages not just by keywords, but predicted conversion likelihood
Generative creative automation driving thousands of micro-variations of CPPs
Increased interplay between web-to-app journeys and personalized store listings
For now, success depends on smart targeting, creative alignment, and relentless iteration. The brands that win in this new era won’t just outspend competitors; they’ll outsmart them through relevance, efficiency, and a store presence engineered for performance.
Voice Search and Ambient Discovery: Adapting ASO to a Screenless, Spoken Future
As voice assistants become more embedded in our everyday lives, from smart speakers to wearable devices, the way users discover and interact with apps is evolving. App discovery is no longer confined to a screen and a search bar. Instead, users are increasingly asking for solutions out loud: “Find me a meditation app” or “Book a table nearby.”
This trend toward ambient, voice-led search means apps need to be discoverable through spoken queries and understand natural language requests. It places a new emphasis on clarity, semantic relevance, and metadata that mirrors conversational phrasing.
App names and descriptions must reflect how people speak, not just how they type
Metadata should include phrases that align with voice query patterns and real-world language
Reviews and ratings (often read aloud by assistants) need to be clear, credible, and compelling
Strategic Implication
The rise of voice doesn’t eliminate traditional ASO. It extends it. Brands must begin adapting their optimization strategy for a future where discoverability happens in a hands-free, multi-modal world, one where clarity, brevity, and natural phrasing win out over dense keyword stacking.
App Intents and Predictive Surfaces
As operating systems become smarter and more anticipatory, app visibility is no longer confined to the app store itself. Platforms like iOS and Android are increasingly surfacing app functionality through features like Siri Suggestions, Spotlight Search, and predictive app actions. These are powered by App Intents – metadata and signals that help the system understand what your app can do and when it should be offered.
In essence, your app can now be discovered without being explicitly searched for, if it fits the context of what a user needs at the right moment.
Why This Matters
App Intents allow apps to:
Appear in Spotlight or voice search based on user behavior and context
Trigger recommended actions like rebooking, ordering, or continuing where a user left off
Surface key functionality (e.g., tracking, booking, paying) without opening the full app
Strategic Opportunity
Optimizing for App Intents isn’t just about technical configuration; it’s about anticipating use cases. What are the moments where your app solves a problem quickly? How can you expose those actions to the OS?
The future of discovery is ambient, predictive, and frictionless. Ensuring your app communicates its capabilities clearly and is structured to surface in those contexts will be a core part of ASO strategy going forward.
What Comes Next
The evolution of App Store Optimization is not about abandoning the fundamentals, it’s about expanding what they mean. Keywords still matter. Visuals still matter. But context, intelligence, and adaptability now define who wins attention and who gets overlooked.
As platforms get smarter, ASO must become more predictive. As user journeys get messier, store listings must become more modular. And as expectations rise, marketers will need to work faster, test more deeply, and collaborate more broadly across product, performance, and creative teams.
If you need help with your ASO strategy, you can learn how our team can help you by contacting us here.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-08-12 19:00:002025-08-12 19:00:00The Future of ASO: Adapting to Intelligent Discovery
